10,296 research outputs found
A Kind of Message-recoverable Fairness Blind Digital Signature Scheme
AbstractBlind digital signature indeed protects interests of the participants to some extent, but the anonymity of blind digital signature present exploit opportunities to attackers. Aiming at problems of current fairness blind digital signature schemes can not simultaneously guarantee completely fairness and can not recover message, the paper proposed a kind of message-recoverable fairness blind signature scheme and analyzed its correctness, security and fairness. The analysis results show that the just can authorize user's identity and correspond it to original signature message with this scheme, and the user can not forge fairness information of just
AGAD: Adversarial Generative Anomaly Detection
Anomaly detection suffered from the lack of anomalies due to the diversity of
abnormalities and the difficulties of obtaining large-scale anomaly data.
Semi-supervised anomaly detection methods are often used to solely leverage
normal data to detect abnormalities that deviated from the learnt normality
distributions. Meanwhile, given the fact that limited anomaly data can be
obtained with a minor cost in practice, some researches also investigated
anomaly detection methods under supervised scenarios with limited anomaly data.
In order to address the lack of abnormal data for robust anomaly detection, we
propose Adversarial Generative Anomaly Detection (AGAD), a self-contrast-based
anomaly detection paradigm that learns to detect anomalies by generating
\textit{contextual adversarial information} from the massive normal examples.
Essentially, our method generates pseudo-anomaly data for both supervised and
semi-supervised anomaly detection scenarios. Extensive experiments are carried
out on multiple benchmark datasets and real-world datasets, the results show
significant improvement in both supervised and semi-supervised scenarios.
Importantly, our approach is data-efficient that can boost up the detection
accuracy with no more than 5% anomalous training data
Pedestrian dynamics in single-file movement of crowd with different age compositions
An aging population is bringing new challenges to the management of escape
routes and facility design in many countries. This paper investigates
pedestrian movement properties of crowd with different age compositions. Three
pedestrian groups are considered: young student group, old people group and
mixed group. It is found that traffic jams occur more frequently in mixed group
due to the great differences of mobilities and self-adaptive abilities among
pedestrians. The jams propagate backward with a velocity 0.4 m/s for global
density around 1.75 m-1 and 0.3 m/s for higher than 2.3 m-1. The fundamental
diagrams of the three groups are obviously different from each other and cannot
be unified into one diagram by direct non-dimensionalization. Unlike previous
studies, three linear regimes in mixed group but only two regimes in young
student group are observed in the headway-velocity relation, which is also
verified in the fundamental diagram. Different ages and mobilities of
pedestrians in a crowd cause the heterogeneity of system and influence the
properties of pedestrian dynamics significantly. It indicates that the density
is not the only factor leading to jams in pedestrian traffic. The composition
of crowd has to be considered in understanding pedestrian dynamics and facility
design.Comment: 11 pages, 13 figures, 3 table
Latent Embeddings for Collective Activity Recognition
Rather than simply recognizing the action of a person individually,
collective activity recognition aims to find out what a group of people is
acting in a collective scene. Previ- ous state-of-the-art methods using
hand-crafted potentials in conventional graphical model which can only define a
limited range of relations. Thus, the complex structural de- pendencies among
individuals involved in a collective sce- nario cannot be fully modeled. In
this paper, we overcome these limitations by embedding latent variables into
feature space and learning the feature mapping functions in a deep learning
framework. The embeddings of latent variables build a global relation
containing person-group interac- tions and richer contextual information by
jointly modeling broader range of individuals. Besides, we assemble atten- tion
mechanism during embedding for achieving more com- pact representations. We
evaluate our method on three col- lective activity datasets, where we
contribute a much larger dataset in this work. The proposed model has achieved
clearly better performance as compared to the state-of-the- art methods in our
experiments.Comment: 6pages, accepted by IEEE-AVSS201
Changes of pore structure and chloride content in cement pastes after pore solution expression
Pore solution expression is a widely accepted approach to extract pore solution of cement-based materials by appllying high pressure. In this study, the variations of pore solution distribution and chloride content in cement pastes before and after pore solution expression were examined. The results showed that the value of chloride concentration index N-c were mostly higher than 1.0 for cement pastes immersed in NaCl solution, and decreased with the chloride concentration of soaking solution and water-to-binder (w/b) ratio. During the pore solution expression, the pores larger than 40 nm were totally removed and the porosity of smaller pore was decreased. Based on a proposed physical model on structure of cement paste, the value of N-c was calculated according to the variations of pore structure and chloride content during pore solution expression. The calculated results showed similar trend as the experimental results obtained by pore solution expression method
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